Abstract
Because of the unconstrained environment of scene text, traditional Optical Character Recognition (OCR) engines fail to achieve satisfactory results. In this paper, we propose a new technique which employs first order Histogram of Oriented Gradient (HOG) through a spatial pyramid. The spatial pyramid can encode the relative spatial layout of the character parts while HOG can only include the local image shape without spatial relation. A feature descriptor combining these two can extracts more useful information from the image for text recognition. Chi-square kernel based Support Vector Machine is employed for classification based on the proposed feature descriptors. The method is tested on three public datasets, namely ICDAR2003 robust reading dataset, Street View Text (SVT) dataset and IIIT 5K-word dataset. The results on these dataset are comparable with the state-of-the-art methods.
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